Introduction
The Worldwide Bureaucracy Indicators (WWBI) database is a unique
cross-national dataset on public sector employment and wages that aims
to fill an information gap, thereby helping researchers, development
practitioners, and policymakers gain a better understanding of the
personnel dimensions of state capability, the footprint of the public
sector within the overall labor market, and the fiscal implications of
the public sector wage bill. The dataset is derived from administrative
data and household surveys, thereby complementing existing, expert
perception-based approaches.
It is classified into 3 different datasets:
wwbi_data : dataset containing measurements of
bureaucratic indicators.
wwbi_series : Contains additional information about
specific indicators, such as indicator_code, which
describes the unique metrics.
wwbi_country : Contains country-level information,
identified by country_code, which helps in enriching the
dataset with country-specific attributes.
In this report, we will be exploring the primary question of:
How do public sector wage structures differ across regions
and gender groups?
To further divide the main question we will be visualizing the
following sub-questions:
How does the wage structure vary over time across regions?
Visualisation used : Heat Map
Are there noticeable trends or divergences in male versus female
wage premiums within each region?
Visualisation used : time series line chart
How does the system of trade influence the wage bill as a
percentage of public expenditure across regions?
Visualisation used : bar chart
The wwbi_full dataset is a refined and comprehensive
version of the Worldwide Bureaucracy Indicators (WWBI) data, created by
merging and cleaning data from multiple tables within the WWBI
database.
Do note that that visualizations 2 and 3 are interactive, hovering
over a point will display information about the datapoint and the graphs
can be zoomed into and panned over.
Data Joins and Sources
The wwbi_full dataset (cleaned) is made by combining the three datasets
mentioned before (wwbi_data, wwbi_series,
wwbi_country). The wwbi_full dataset contains
detailed information on economic indicators by country. Key variables
used in this analysis include:
year: The year of the recorded data, formatted as a
date (first day of the year).
country_code: The country code in uppercase format,
uniquely identifying each country.
region: Geographic region to which the country
belongs.
income_group: The income classification of the
country, categorized as “Low income,” “Lower middle income,” “Upper
middle income,” and “High income.” This is stored as an ordered factor
to facilitate comparisons across income levels.
system_of_trade: The system used for trading,
formatted as a factor variable.
value: The recorded value of the economic indicator
for the specified year and country.
short_name and long_name: The short and
full names of each country.
x2_alpha_code: An alternate country code.
indicator_code and indicator_name:
Codes and names identifying the specific economic indicator
measured.
Importing Datasets and Libraries
library(tidyverse)
library(readxl)
library(lubridate)
library(stringr)
library(dplyr)
library(ggthemes)
library(sf)
library(rnaturalearth)
library(rnaturalearthdata)
library(plotly)
wwbi_data <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2024/2024-04-30/wwbi_data.csv')
wwbi_series <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2024/2024-04-30/wwbi_series.csv')
wwbi_country <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2024/2024-04-30/wwbi_country.csv')
Data Cleaning and Summary
To prepare the wwbi_full dataset for analysis, the
following cleaning steps were applied:
Joining and Selection:
The primary data tables (wwbi_data,
wwbi_series, and wwbi_country) were merged
using common columns (country_code and
indicator_code). Only relevant columns needed for analysis
were retained.
Data Transformation:
- Date Formatting: The
year column was
converted to a Date format (January 1st of each year) to
facilitate time-based analysis.
- Factor Levels: The
income_group was
set as an ordered factor to allow comparisons in ascending order of
income levels.
- Standardization: The
country_code
column was converted to uppercase for consistency across country
codes.
Handling Duplicates and Missing Values:
- Duplicates: Duplicate entries were removed to
ensure unique records.
- Missing Values: Rows containing missing values were
removed. This step was justified as the presence of
NA
values might skew the results or cause inconsistencies in the
analysis.
Validation:
After cleaning, the dataset structure was inspected to verify that all
variables were formatted as expected and ready for analysis.
wwbi_full <- wwbi_data %>%
inner_join(wwbi_series, by = "indicator_code") %>%
inner_join(wwbi_country, by = "country_code") %>%
select(year,country_code,region, income_group, system_of_trade, value, short_name, long_name, x2_alpha_code, indicator_code, indicator_name) %>%
mutate(year=as.Date(paste0(year,"-01-01")),income_group=factor(income_group,levels=c("Low income","Lower middle income","Upper middle income","High income"),ordered=TRUE),system_of_trade=as.factor(system_of_trade),country_code=str_to_upper(country_code)) %>% #converting variables into appropriate types
distinct() %>% #keep only unique data
na.omit() #remove missing data
head(wwbi_full)
## # A tibble: 6 × 11
## year country_code region income_group system_of_trade value short_name
## <date> <chr> <chr> <ord> <fct> <dbl> <chr>
## 1 2007-01-01 AFG South A… Low income General trade … 1.11 Afghanist…
## 2 2013-01-01 AFG South A… Low income General trade … 0.649 Afghanist…
## 3 2007-01-01 AFG South A… Low income General trade … 0.533 Afghanist…
## 4 2013-01-01 AFG South A… Low income General trade … 0.433 Afghanist…
## 5 2007-01-01 AFG South A… Low income General trade … 0.858 Afghanist…
## 6 2013-01-01 AFG South A… Low income General trade … 0.661 Afghanist…
## # ℹ 4 more variables: long_name <chr>, x2_alpha_code <chr>,
## # indicator_code <chr>, indicator_name <chr>
str(wwbi_full)
## tibble [136,290 × 11] (S3: tbl_df/tbl/data.frame)
## $ year : Date[1:136290], format: "2007-01-01" "2013-01-01" ...
## $ country_code : chr [1:136290] "AFG" "AFG" "AFG" "AFG" ...
## $ region : chr [1:136290] "South Asia" "South Asia" "South Asia" "South Asia" ...
## $ income_group : Ord.factor w/ 4 levels "Low income"<"Lower middle income"<..: 1 1 1 1 1 1 1 1 1 1 ...
## $ system_of_trade: Factor w/ 2 levels "General trade system",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ value : num [1:136290] 1.11 0.649 0.533 0.433 0.858 ...
## $ short_name : chr [1:136290] "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
## $ long_name : chr [1:136290] "Islamic State of Afghanistan" "Islamic State of Afghanistan" "Islamic State of Afghanistan" "Islamic State of Afghanistan" ...
## $ x2_alpha_code : chr [1:136290] "AF" "AF" "AF" "AF" ...
## $ indicator_code : chr [1:136290] "BI.WAG.PRVS.FM.SM" "BI.WAG.PRVS.FM.SM" "BI.WAG.PRVS.FM.MD" "BI.WAG.PRVS.FM.MD" ...
## $ indicator_name : chr [1:136290] "Female to male wage ratio in the private sector (using mean)" "Female to male wage ratio in the private sector (using mean)" "Female to male wage ratio in the private sector (using median)" "Female to male wage ratio in the private sector (using median)" ...
## - attr(*, "na.action")= 'omit' Named int [1:5695] 1487 1488 1489 1490 1491 1492 1493 1494 13955 13956 ...
## ..- attr(*, "names")= chr [1:5695] "1487" "1488" "1489" "1490" ...
Visualization 1: How does the wage structure vary over time across
regions?
About the Visual
The following heatmap illustrates a global average wage structure
across countries at 5-year intervals. Each panel in the heatmap
represents a specific time frame, spanning from 1995 to 2020. This
allows for the capturing of temporal trends, enabling comparisons of
wage dynamics across both time and geography. This analysis is derived
from the BI.WAG.TOTL.GD.ZS indicator, which measures wages as a
percentage of GDP, offering a relative sense of how much of a country’s
economic output translates into labor earnings.
In order to prepare the data for the visualization:
The wage_data_summary is the summarized data
aggregated by country and 5-year intervals, including average wages
(avg_wage).
world refers to the geographic shapefile containing
country boundaries and metadata.
the final joined dataset, world_wage_map, combines
spatial boundaries with summarized wage data for visualization.
A heat map visualization of the average wage structure is ideal as it
gives us clarity of the changes over time across countries, and their
magnitude (as observed by respective intensity on the color scale).
# Load world map data
world <- ne_countries(scale = "medium", returnclass = "sf")
# Summarize the wage data by country and 5-year intervals
wage_data_summary <- wwbi_full %>%
filter(indicator_code == "BI.WAG.TOTL.GD.ZS") %>%
mutate(
year_interval = cut(
lubridate::year(year), # Extract numeric year
breaks = seq(1960, 2025, by = 5), # 5-year intervals
labels = paste(seq(1960, 2020, by = 5), seq(1965, 2025, by = 5), sep = "-"), # Interval labels
include.lowest = TRUE)) %>% # Include the first year in the first interval
group_by(country_code, year_interval) %>%
summarize(avg_wage = mean(value, na.rm = TRUE), .groups = "drop") %>%
ungroup()
# Join the summarized wage data with the world map data
world_wage_map <- world %>%
left_join(wage_data_summary, by = c("iso_a3" = "country_code")) %>%
filter(!is.na(avg_wage)) # Remove rows with NA avg_wage
# Plot the heatmaps for different 5-year intervals
ggplot(world_wage_map) +
geom_sf(aes(fill = avg_wage, text = paste0("<b>Country :</b> ", admin, "<br>",
"<b>Year Interval:</b> ", year_interval, "<br>",
"<b>Avergae wage :</b> ", avg_wage, "<br>")), color = "black") +
facet_wrap(~ year_interval) + # Facet by 5-year intervals
scale_fill_viridis_c(name = "Average Wage", direction = -1) + # Color scale
labs(title = "Average Wage Structure Across Countries by 5-Year Intervals",
caption = "Source: WWBI Data") +
theme_minimal() +
theme(strip.text = element_text(size = 10), # Adjust facet label size
plot.title = element_text(hjust = 0.5), # Center the title
axis.text.x = element_text(angle = 90),
legend.position = "top")
# saving the plot as a jpeg file for easy reference later
ggsave("../figures/project_visual1.jpeg")
Primary Insights, Observations and Explanation of Results
Regional Wage Disparity
The visualization brings to light the stark regional disparities in wage
levels across the globe, with significant variations between high-income
and low-income regions. Developed regions, such as North America and
Western Europe, consistently display higher wage levels across the
years, as represented by darker shades on the heatmap. Many factors
contribute to sustained higher wages over time. These regions have
well-established labour markets, higher productivity, and advanced
economic structures, giving them a competitive edge over other lesser
developed countries.
In contrast, regions in Sub-Saharan Africa, parts of South Asia, and
portions of Latin America are characterized by lighter shades,
indicating significantly lower average wages. These regions often face
structural challenges such as weaker industrial bases, limited access to
global markets, lower productivity, and higher rates of informal
employment. The persistent differences in wage levels between these
regions and their developed counterparts reflect entrenched economic
inequalities. These concerns have remained largely unaddressed, or shown
negligible change over the years.
The wage disparities also underline the relationship between regional
development levels and labor remuneration. While high-income regions
benefit from technological advancements, robust infrastructure, and
effective governance, low-income regions are often constrained by
limited resources, political instability, and lack of investment in
human capital. This consistent wage gap raises important questions about
global inequality and the mechanisms required to foster more inclusive
economic growth.
Temporal Trends
Over the analyzed 5-year intervals, some regions exhibit gradual wage
growth, as indicated by the increasing intensity of colors in parts of
the map. For example, East Asia and certain areas in South America show
consistent improvements in wage levels, signaling their progression as
emerging economies. These trends are likely attributed to factors such
as industrial expansion, integration into global trade networks, and
targeted domestic policies aimed at improving labor conditions.
Conversely, several low-income regions demonstrate stagnation in wage
levels, with minimal changes in color intensity across intervals.
Sub-Saharan Africa and parts of South Asia remain particularly
vulnerable, with limited progress in economic development and labor
market improvements. These regions’ stagnation highlights the challenges
of breaking out of cycles of low productivity, poor infrastructure, and
reliance on informal employment sectors.
The visualization suggests that while globalization and economic reforms
have spurred growth in some regions, others have not experienced the
same benefits. This dichotomy underscores the uneven nature of global
economic progress and the need for more targeted development
strategies.
Visualization 2: Are there noticeable trends or divergences in male
versus female wage premiums within each region?
About the Visual
This visualization consists of a multiple line plot to display and
compare changes in the Wage Premiums over time by gender, faceted by
region.
In order to prepare the data for the visual:
The wage_premium_time_data tibble was created which
kept only the data concerning public sector wage premiums by
gender.
The gender variable was extracted from the
indicator_code to clearly distinguish between the wage
premiums in the plot (through color coding and separate lines).
The average wage premium avg_wage_premium was then
calculated for each group of region, gender
and year, ignoring any missing values.
The plot selected is ideal for the question as it clearly showcases
the trends across each region, with the separate lines for male and
female wage premiums to highlight gender-based differences. The lines
being colored by gender helps one visually track any
divergence or convergence between the premiums over time. The facets
highlight regional differences and similarities in wage structures.
# Prepare the data, filtering only relevant rows
wage_premium_time_data <- wwbi_full %>%
filter(indicator_code==c("BI.WAG.PREM.PB.MA","BI.WAG.PREM.PB.FE")) %>%
mutate(
gender=ifelse(indicator_code=="BI.WAG.PREM.PB.FE","Female","Male"))%>%
group_by(region, gender, year) %>%
summarize(avg_wage_premium = mean(value, na.rm = TRUE)) %>%
ungroup()
# Create the faceted line plot
visualization2 <- ggplot(wage_premium_time_data, aes(x=year,y=avg_wage_premium,color=gender,group=gender, text = paste0("<b>Average wage premium:</b> ", round(avg_wage_premium,3), "<br>",
"<b>Year:</b> ", year(year), "<br>",
"<b>Gender:</b> ", gender, "<br>"))) +
geom_line(size=0.8) +
geom_point(size=1.3) +
labs(title="Public Sector Wage Premium Trends Over Time by Region and Gender",
x="Year",y="Average Wage Premium",color="Gender",caption = "Source: WWBI Data") +
scale_color_manual(values=c("Male"="lightskyblue","Female"="lightpink2")) +
theme_minimal() +
facet_wrap(~region) +
theme(
plot.title = element_text(hjust = 0.5,face="bold", size = 10),
axis.text.x = element_text(angle = 45, hjust = 1),
strip.text = element_text(face = "bold"))
ggplotly(visualization2, tooltip = "text")
# saving the plot as a jpeg file for easy reference later
ggsave("../figures/project_visual2.jpeg")
Primary Insights and Observations
The line plot aimed to convey 3 main insights:-
Trend consistency across regions: By tracking
each region individually, we can observe whether wage premiums have
followed consistent patterns over time within the region.
It can be seen that regions such as Latin America & Caribbean and
Europe & Central Asia show stable premiums over time, indicating
potentially consistent public sector wage policies. On the other hand,
South Asia and Sub-Saharan Africa show High volatility in wage premiums,
suggesting economic instability or frequent policy changes that impact
public sector wages.
Gender-based differences: The plot displays if
there are sustained differences between male and female wage premiums in
each region, or if the differences converge or diverge over time.
Regions such as South Asia have the female wage premium higher than the
male wage premium frequently, indicating a larger relative advantage for
women in the public sector. In other regions like Middle East &
North Africa, the male wage premium tends to be consistently higher,
suggesting potential gender-based wage practices.
Temporal patterns: The plot helps to identify
trends in wage premiums, be it peaks, declines or steadiness. This may
help identify and correlate with broader economic or policy changes over
time.
The wage premium for both genders remain steady in regions like Latin
America & Caribbean and decline in others, like Middle East &
North Africa. This indicates an overall reduction in the wage advantage
of the public sector relative to the private sector.
Note: North America is excluded from the region-based analysis as the
data does not contain information regarding gender-based wage premiums
for the region
Explanation of Results
The data patterns observed in public sector wage premiums across
regions and genders may appear so due to a variety of reasons, such
as:
Wage Policies and Market Structure
Some regions may exhibit higher wage premiums due to the structure of
their labor markets. Regions where public sector jobs offer more
stability and benefits may see higher public sector premiums. In
contrast, regions with a competitive private sector may have smaller or
negative public sector premiums.
Cultural and Structural Factors
Social norms around gender roles and labour structures may contribute to
the disparities. For example, regions such as South Asia showcase higher
female wage premiums as they tend to pick up and benefit more from
public sector jobs when compared to men. These include roles such as
teachers and healthcare workers, which are stereotypically occupied by
women. This may also be a result of incentivization of women in the
workforce.
Economic Stability
On observation, it can be assumed that stable government economic
regulations (such as in Europe & Central Asia) showcase stable
public sector wage premiums and gender-equality, when compared to
regions experiencing economic and other turbulence like Sub-Saharan
Africa.
Visualization 3: How does the system of trade influence the wage
bill as a percentage of public expenditure across regions?
About the Visual
The visualization utilized is a diverging bar chart to depict
relation between the wage bill as a percentage of public expenditure and
the regions.
In order to prepared the data for the visual:
The wage_system_of_trade tibble was created, which
contains the filtered data so that we can specifically look at the data
corresponding to “BI.WAG.TOTL.PB.ZS” indicator_code which
is “Wage bill as a percentage of Public Expenditure”.
The Total variable was calculated based on grouping
by region and system_of_trade, it is a
summation of all the values.
The plot selected is ideal for the question as it clearly showcases
the differences based on system_of_trade. Additionally,
filling it by region helps one observe the segregation of
the Total for each region.
wage_system_of_trade <- wwbi_full %>%
filter(indicator_code == "BI.WAG.TOTL.PB.ZS") %>%
group_by(region, system_of_trade) %>%
summarise(Total = sum(value, na.rm = TRUE)) %>%
ungroup()
visualization3 <- ggplot(wage_system_of_trade , aes(y = region, x = Total, fill = system_of_trade, text = paste0("<b>System of Trade :</b> ", system_of_trade, "<br>", "<b>Rounded wage bill as % of public expenditure:</b> ", round(Total), "<br>"))) +
geom_col(data = wage_system_of_trade %>% filter(system_of_trade == "General trade system")) +
geom_col(data = wage_system_of_trade %>% filter(system_of_trade == "Special trade system"), aes(x = -Total)) +
labs(y = "", x = "Wage bill as a % of Public Expenditure", fill = "System of Trade", caption = "Source:Worldwide Bureaucracy Indicators (WWBI) dataset from the World Bank", title = "System of trade vs the wage bill(%) of public expense across regions") +
scale_x_continuous(labels = abs) +
scale_fill_manual(values=c("General trade system" = "maroon", "Special trade system" = "darkorange"))+
theme(plot.title = element_text(hjust = 0.5, face = "bold", size = 10))
ggplotly(visualization3, tooltip = "text")
# saving the plot as a jpeg file for easy reference later
ggsave("../figures/project_visual3.jpeg")
Primary Insights and Observations
- The relationship between wage bills and the system of
trade
There is no definitive trend that is seen that correlates between the
wage bill variable and system of trade variable. This shows that there
is no relation between the procedure for trade and goods and its
influence on the public expenditure as a wage bill.
- The relationship between the type of system of trade and the
region
There are 7 regions that have been plotted for. For certain regions
there is a surge in the public expenditure relating to the type of
system of trade. As seen in Sub-Saharan Africa and East Asia &
Pacific, where the General trade system has more wage bill as a
percentage of public expenditure compared to the special trade system
and the opposite is seen for Latin America & Caribbean. While South
Asia and North America have opted for no special trade system procedures
for goods. An interesting trend is seen for the Middle East & North
America and Latin America & Caribbean where there is almost equal
influence of the two trade systems set on public expenditure.
- When seen as an overview the influence of
system_of_trade on wage bill as a percentage of public
expenditure is almost equal but the different trends are seen when
observed region wise.
Explanation of Results
The data patterns observed in wage bills across regions and systems
of trade may appear so due to a variety of reasons, such as:
- Influence of Trade Systems and Regional
Policies
The type of trade system in place—whether a general or special trade
system—can be shaped by regional policies that influence public
expenditure. In the Middle East & North Africa and Latin America
& Caribbean regions, specific trade policies help stabilize the
impact of the chosen trade system on wage-related public spending. These
regional policies can either increase or decrease the wage bill portion
of public expenditure.
- Variations in Trade Systems and Cost
Allocation
Regions with more strict trade policies often incur higher wage costs,
as their complex requirements demand more resources and regulatory
oversight. In contrast, countries with simpler trade systems face lower
wage bills due to reduced vetting and resource needs.
- Size of the Public Sector and Economic
Structure
The impact on wage bills also varies with the size of the public sector
and economic structure of each region. For example, North America has a
relatively smaller public sector, resulting in lower wage-related public
spending. In contrast, regions like Sub-Saharan Africa and East Asia
& Pacific have larger public sectors and rely more heavily on
general trade systems, leading to higher wage bills.
Summarising overall pattern
The analysis of income structures and public sector wage
distributions across multiple factors is explained using the three
visualizations. Each visualization answers a specific part of the main
question, helping to give a clear understanding of global income
structures.
The heatmap visualization shows how average wage structures change
over time across different regions, using 5-year intervals. It reveals
major differences in wage levels between regions. The line plot examines
how male and female wage premiums in the public sector differ across
regions and change over time. It highlights several important patterns
such as stable wage premiums over time, gender differences and
fluctuations due to policy changes. The bar chart explores how different
trade systems (general and special) affect wage bills as a percentage of
public expenditure across regions. The analysis shows no universal trend
linking trade systems to public wage bill, which suggests that trade
systems alone do not directly determine how much governments spend on
wages. Regional differences are highlighted by showcasing the balanced
impact of regional trade policies and public sector structures in
shaping wage bills.
Together, these visualizations provide insights into how regional,
gender-based, and systemic factors affect income structures and public
sector wage distributions. High-income regions consistently have higher
wage levels, while emerging economies show slow improvements, and
low-income regions face persistent challenges. Gender wage dynamics vary
widely, reflecting cultural and economic differences across regions.
Finally, the influence of trade systems on public wage expenditure is
complex and varies by region, shaped by local policies and economic
structures. These findings emphasize the need for focused strategies to
reduce inequalities and encourage fair economic growth.
Teamwork
The data cleaning code and references was done individually by
everyone and then compiled.
Vedam Akshata Jaishankar
1. Visualization 2 code
2. Visualization 2 summary
3. Rmd Compilation
Muthukrishnan Navya
1. Visualization 3 code
2. Visualization 3 summary
Jain Paridhi
1. Visualization 1 code
2. Visualization 1 summary
Mitra Reet
1. Visualization 2 code
2. Data Cleaning and Summary
3. Overall Patterns Summarized
Harihara Venkatesan Vaishnavi
1. Visualization 1 code
2. Introduction